Overview

Dataset statistics

Number of variables29
Number of observations2736
Missing cells27956
Missing cells (%)35.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory654.7 KiB
Average record size in memory245.0 B

Variable types

Numeric5
Text9
DateTime3
Categorical6
Unsupported6

Dataset

Description개방자치단체코드,관리번호,인허가일자,인허가취소일자,영업상태코드,영업상태명,상세영업상태코드,상세영업상태명,폐업일자,휴업시작일자,휴업종료일자,재개업일자,전화번호,소재지면적,소재지우편번호,지번주소,도로명주소,도로명우편번호,사업장명,최종수정일자,데이터갱신구분,데이터갱신일자,업태구분명,좌표정보(X),좌표정보(Y),비상시설위치,시설구분명,시설명_건물명,해제일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-16149/S/1/datasetView.do

Alerts

영업상태코드 is highly imbalanced (54.9%)Imbalance
영업상태명 is highly imbalanced (54.9%)Imbalance
상세영업상태코드 is highly imbalanced (54.9%)Imbalance
상세영업상태명 is highly imbalanced (54.9%)Imbalance
데이터갱신구분 is highly imbalanced (76.4%)Imbalance
인허가취소일자 has 2478 (90.6%) missing valuesMissing
폐업일자 has 2478 (90.6%) missing valuesMissing
휴업시작일자 has 2736 (100.0%) missing valuesMissing
휴업종료일자 has 2736 (100.0%) missing valuesMissing
재개업일자 has 2736 (100.0%) missing valuesMissing
전화번호 has 2736 (100.0%) missing valuesMissing
소재지우편번호 has 2736 (100.0%) missing valuesMissing
도로명우편번호 has 56 (2.0%) missing valuesMissing
업태구분명 has 2736 (100.0%) missing valuesMissing
좌표정보(X) has 57 (2.1%) missing valuesMissing
좌표정보(Y) has 57 (2.1%) missing valuesMissing
비상시설위치 has 1886 (68.9%) missing valuesMissing
시설명_건물명 has 1886 (68.9%) missing valuesMissing
해제일자 has 2641 (96.5%) missing valuesMissing
소재지면적 is highly skewed (γ1 = 47.78082801)Skewed
좌표정보(X) is highly skewed (γ1 = 35.62347965)Skewed
좌표정보(Y) is highly skewed (γ1 = 48.44874631)Skewed
관리번호 has unique valuesUnique
휴업시작일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
휴업종료일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
재개업일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전화번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지우편번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 06:32:24.847797
Analysis finished2024-05-11 06:32:27.081421
Duration2.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

개방자치단체코드
Real number (ℝ)

Distinct26
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3122529.2
Minimum3000000
Maximum4160000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2024-05-11T15:32:27.179569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3010000
Q13070000
median3120000
Q33180000
95-th percentile3230000
Maximum4160000
Range1160000
Interquartile range (IQR)110000

Descriptive statistics

Standard deviation71891.391
Coefficient of variation (CV)0.023023448
Kurtosis14.536667
Mean3122529.2
Median Absolute Deviation (MAD)60000
Skewness1.0642179
Sum8.54324 × 109
Variance5.1683721 × 109
MonotonicityNot monotonic
2024-05-11T15:32:27.405330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3100000 199
 
7.3%
3150000 169
 
6.2%
3060000 163
 
6.0%
3210000 152
 
5.6%
3120000 140
 
5.1%
3230000 137
 
5.0%
3070000 124
 
4.5%
3030000 123
 
4.5%
3140000 119
 
4.3%
3110000 116
 
4.2%
Other values (16) 1294
47.3%
ValueCountFrequency (%)
3000000 113
4.1%
3010000 84
3.1%
3020000 68
2.5%
3030000 123
4.5%
3040000 55
 
2.0%
3050000 56
 
2.0%
3060000 163
6.0%
3070000 124
4.5%
3080000 104
3.8%
3090000 104
3.8%
ValueCountFrequency (%)
4160000 1
 
< 0.1%
3240000 75
2.7%
3230000 137
5.0%
3220000 81
3.0%
3210000 152
5.6%
3200000 110
4.0%
3190000 97
3.5%
3180000 111
4.1%
3170000 69
2.5%
3160000 87
3.2%

관리번호
Text

UNIQUE 

Distinct2736
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
2024-05-11T15:32:27.720311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.999269
Min length16

Characters and Unicode

Total characters49246
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2736 ?
Unique (%)100.0%

Sample

1st row3080000-S202300006
2nd row3080000-S202400001
3rd row3030000-S202300027
4th row3160000-S202200004
5th row3100000-S201700003
ValueCountFrequency (%)
3080000-s202300006 1
 
< 0.1%
3180000-s202300001 1
 
< 0.1%
3120000-s200100009 1
 
< 0.1%
3210000-s200800140 1
 
< 0.1%
3120000-s200100018 1
 
< 0.1%
3120000-s200100016 1
 
< 0.1%
3180000-s202100004 1
 
< 0.1%
3150000-s201100011 1
 
< 0.1%
3180000-s201000004 1
 
< 0.1%
3160000-s202300001 1
 
< 0.1%
Other values (2726) 2726
99.6%
2024-05-11T15:32:28.351861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 25531
51.8%
1 4391
 
8.9%
2 4012
 
8.1%
3 3927
 
8.0%
- 2736
 
5.6%
S 2736
 
5.6%
9 1935
 
3.9%
8 902
 
1.8%
5 794
 
1.6%
4 775
 
1.6%
Other values (2) 1507
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43774
88.9%
Dash Punctuation 2736
 
5.6%
Uppercase Letter 2736
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 25531
58.3%
1 4391
 
10.0%
2 4012
 
9.2%
3 3927
 
9.0%
9 1935
 
4.4%
8 902
 
2.1%
5 794
 
1.8%
4 775
 
1.8%
6 757
 
1.7%
7 750
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 2736
100.0%
Uppercase Letter
ValueCountFrequency (%)
S 2736
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 46510
94.4%
Latin 2736
 
5.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 25531
54.9%
1 4391
 
9.4%
2 4012
 
8.6%
3 3927
 
8.4%
- 2736
 
5.9%
9 1935
 
4.2%
8 902
 
1.9%
5 794
 
1.7%
4 775
 
1.7%
6 757
 
1.6%
Latin
ValueCountFrequency (%)
S 2736
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49246
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 25531
51.8%
1 4391
 
8.9%
2 4012
 
8.1%
3 3927
 
8.0%
- 2736
 
5.6%
S 2736
 
5.6%
9 1935
 
3.9%
8 902
 
1.8%
5 794
 
1.6%
4 775
 
1.6%
Other values (2) 1507
 
3.1%
Distinct1004
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
Minimum1974-01-01 00:00:00
Maximum2024-03-11 00:00:00
2024-05-11T15:32:28.639977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:28.876873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Text

MISSING 

Distinct99
Distinct (%)38.4%
Missing2478
Missing (%)90.6%
Memory size21.5 KiB
2024-05-11T15:32:29.306043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.255814
Min length5

Characters and Unicode

Total characters2130
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)18.6%

Sample

1st row2023-07-17
2nd row2023-07-17
3rd row2023-07-20
4th row2019-03-08
5th row2019-03-08
ValueCountFrequency (%)
2023-11-06 35
 
13.6%
43053 10
 
3.9%
42730 10
 
3.9%
2023-11-21 9
 
3.5%
2023-12-26 7
 
2.7%
42401 6
 
2.3%
2023-11-08 6
 
2.3%
2023-12-28 6
 
2.3%
2023-07-31 5
 
1.9%
42754 5
 
1.9%
Other values (89) 159
61.6%
2024-05-11T15:32:29.915683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1794
84.2%
Dash Punctuation 336
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 490
27.3%
0 350
19.5%
1 323
18.0%
3 230
12.8%
4 129
 
7.2%
6 79
 
4.4%
7 71
 
4.0%
8 56
 
3.1%
5 38
 
2.1%
9 28
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
1
2478 
4
258 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2478
90.6%
4 258
 
9.4%

Length

2024-05-11T15:32:30.132999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:30.282780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2478
90.6%
4 258
 
9.4%

영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
영업/정상
2478 
취소/말소/만료/정지/중지
258 

Length

Max length14
Median length5
Mean length5.8486842
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업/정상
2nd row영업/정상
3rd row영업/정상
4th row영업/정상
5th row영업/정상

Common Values

ValueCountFrequency (%)
영업/정상 2478
90.6%
취소/말소/만료/정지/중지 258
 
9.4%

Length

2024-05-11T15:32:30.442621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:30.686076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업/정상 2478
90.6%
취소/말소/만료/정지/중지 258
 
9.4%

상세영업상태코드
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
18
2478 
19
258 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row18
2nd row18
3rd row18
4th row18
5th row18

Common Values

ValueCountFrequency (%)
18 2478
90.6%
19 258
 
9.4%

Length

2024-05-11T15:32:30.879369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:31.045173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
18 2478
90.6%
19 258
 
9.4%

상세영업상태명
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
사용중
2478 
사용중지
258 

Length

Max length4
Median length3
Mean length3.0942982
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사용중
2nd row사용중
3rd row사용중
4th row사용중
5th row사용중

Common Values

ValueCountFrequency (%)
사용중 2478
90.6%
사용중지 258
 
9.4%

Length

2024-05-11T15:32:31.196574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:31.364374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사용중 2478
90.6%
사용중지 258
 
9.4%

폐업일자
Text

MISSING 

Distinct99
Distinct (%)38.4%
Missing2478
Missing (%)90.6%
Memory size21.5 KiB
2024-05-11T15:32:31.735312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length8.255814
Min length5

Characters and Unicode

Total characters2130
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)18.6%

Sample

1st row2023-07-17
2nd row2023-07-17
3rd row2023-07-20
4th row2019-03-08
5th row2019-03-08
ValueCountFrequency (%)
2023-11-06 35
 
13.6%
43053 10
 
3.9%
42730 10
 
3.9%
2023-11-21 9
 
3.5%
2023-12-26 7
 
2.7%
42401 6
 
2.3%
2023-11-08 6
 
2.3%
2023-12-28 6
 
2.3%
2023-07-31 5
 
1.9%
42754 5
 
1.9%
Other values (89) 159
61.6%
2024-05-11T15:32:32.340893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1794
84.2%
Dash Punctuation 336
 
15.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 490
27.3%
0 350
19.5%
1 323
18.0%
3 230
12.8%
4 129
 
7.2%
6 79
 
4.4%
7 71
 
4.0%
8 56
 
3.1%
5 38
 
2.1%
9 28
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 336
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2130
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 490
23.0%
0 350
16.4%
- 336
15.8%
1 323
15.2%
3 230
10.8%
4 129
 
6.1%
6 79
 
3.7%
7 71
 
3.3%
8 56
 
2.6%
5 38
 
1.8%

휴업시작일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB

휴업종료일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB

재개업일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB

전화번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB

소재지면적
Real number (ℝ)

SKEWED 

Distinct2353
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10955.784
Minimum1
Maximum4105106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2024-05-11T15:32:32.570986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile388.2175
Q11487.61
median4419.5
Q39689.25
95-th percentile36375.5
Maximum4105106
Range4105105
Interquartile range (IQR)8201.64

Descriptive statistics

Standard deviation80775.462
Coefficient of variation (CV)7.3728599
Kurtosis2415.9036
Mean10955.784
Median Absolute Deviation (MAD)3367.53
Skewness47.780828
Sum29975026
Variance6.5246753 × 109
MonotonicityNot monotonic
2024-05-11T15:32:32.766709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4000.0 13
 
0.5%
1.0 12
 
0.4%
2500.0 11
 
0.4%
2000.0 11
 
0.4%
4500.0 11
 
0.4%
1200.0 10
 
0.4%
600.0 10
 
0.4%
5500.0 9
 
0.3%
2451.0 8
 
0.3%
500.0 7
 
0.3%
Other values (2343) 2634
96.3%
ValueCountFrequency (%)
1.0 12
0.4%
44.0 1
 
< 0.1%
62.88 1
 
< 0.1%
80.4 1
 
< 0.1%
88.0 1
 
< 0.1%
99.0 1
 
< 0.1%
99.17 1
 
< 0.1%
107.0 1
 
< 0.1%
120.64 1
 
< 0.1%
124.0 1
 
< 0.1%
ValueCountFrequency (%)
4105106.0 1
< 0.1%
361211.0 1
< 0.1%
338733.0 1
< 0.1%
327436.86 1
< 0.1%
308775.0 1
< 0.1%
279901.0 1
< 0.1%
261492.0 1
< 0.1%
165290.0 1
< 0.1%
136343.0 1
< 0.1%
126028.0 1
< 0.1%

소재지우편번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB
Distinct2436
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
2024-05-11T15:32:33.229306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length40
Mean length22.086988
Min length14

Characters and Unicode

Total characters60430
Distinct characters421
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2289 ?
Unique (%)83.7%

Sample

1st row서울특별시 강북구 미아동 1353 SK북한산시티아파트
2nd row서울특별시 강북구 미아동 1364 연이빌딩
3rd row서울특별시 성동구 성수동2가 308-4 서울숲코오롱디지털타워
4th row서울특별시 구로구 가리봉동 118-11
5th row서울특별시 노원구 월계동 447번지 1호
ValueCountFrequency (%)
서울특별시 2735
 
21.6%
1호 229
 
1.8%
노원구 199
 
1.6%
강서구 169
 
1.3%
중랑구 163
 
1.3%
서초구 145
 
1.1%
서대문구 139
 
1.1%
송파구 136
 
1.1%
성북구 124
 
1.0%
성동구 123
 
1.0%
Other values (2497) 8508
67.2%
2024-05-11T15:32:33.819779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9936
 
16.4%
3280
 
5.4%
3127
 
5.2%
2898
 
4.8%
2800
 
4.6%
2764
 
4.6%
2738
 
4.5%
2737
 
4.5%
2270
 
3.8%
2175
 
3.6%
Other values (411) 25705
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39957
66.1%
Decimal Number 10078
 
16.7%
Space Separator 9936
 
16.4%
Dash Punctuation 269
 
0.4%
Uppercase Letter 119
 
0.2%
Lowercase Letter 31
 
0.1%
Other Punctuation 13
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Close Punctuation 11
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3280
 
8.2%
3127
 
7.8%
2898
 
7.3%
2800
 
7.0%
2764
 
6.9%
2738
 
6.9%
2737
 
6.8%
2270
 
5.7%
2175
 
5.4%
1004
 
2.5%
Other values (356) 14164
35.4%
Uppercase Letter
ValueCountFrequency (%)
C 22
18.5%
M 18
15.1%
D 17
14.3%
S 12
10.1%
K 11
9.2%
T 5
 
4.2%
N 4
 
3.4%
V 4
 
3.4%
I 3
 
2.5%
O 3
 
2.5%
Other values (11) 20
16.8%
Lowercase Letter
ValueCountFrequency (%)
e 14
45.2%
r 3
 
9.7%
o 3
 
9.7%
t 2
 
6.5%
l 2
 
6.5%
a 1
 
3.2%
m 1
 
3.2%
p 1
 
3.2%
h 1
 
3.2%
c 1
 
3.2%
Other values (2) 2
 
6.5%
Decimal Number
ValueCountFrequency (%)
1 2085
20.7%
2 1141
11.3%
3 1119
11.1%
4 1005
10.0%
5 929
9.2%
7 843
8.4%
6 824
 
8.2%
0 755
 
7.5%
8 696
 
6.9%
9 681
 
6.8%
Other Punctuation
ValueCountFrequency (%)
, 10
76.9%
/ 2
 
15.4%
. 1
 
7.7%
Open Punctuation
ValueCountFrequency (%)
( 10
90.9%
[ 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 10
90.9%
] 1
 
9.1%
Letter Number
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
9936
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 269
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39956
66.1%
Common 20321
33.6%
Latin 152
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3280
 
8.2%
3127
 
7.8%
2898
 
7.3%
2800
 
7.0%
2764
 
6.9%
2738
 
6.9%
2737
 
6.9%
2270
 
5.7%
2175
 
5.4%
1004
 
2.5%
Other values (355) 14163
35.4%
Latin
ValueCountFrequency (%)
C 22
14.5%
M 18
11.8%
D 17
11.2%
e 14
 
9.2%
S 12
 
7.9%
K 11
 
7.2%
T 5
 
3.3%
N 4
 
2.6%
V 4
 
2.6%
I 3
 
2.0%
Other values (25) 42
27.6%
Common
ValueCountFrequency (%)
9936
48.9%
1 2085
 
10.3%
2 1141
 
5.6%
3 1119
 
5.5%
4 1005
 
4.9%
5 929
 
4.6%
7 843
 
4.1%
6 824
 
4.1%
0 755
 
3.7%
8 696
 
3.4%
Other values (10) 988
 
4.9%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39956
66.1%
ASCII 20471
33.9%
Number Forms 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9936
48.5%
1 2085
 
10.2%
2 1141
 
5.6%
3 1119
 
5.5%
4 1005
 
4.9%
5 929
 
4.5%
7 843
 
4.1%
6 824
 
4.0%
0 755
 
3.7%
8 696
 
3.4%
Other values (43) 1138
 
5.6%
Hangul
ValueCountFrequency (%)
3280
 
8.2%
3127
 
7.8%
2898
 
7.3%
2800
 
7.0%
2764
 
6.9%
2738
 
6.9%
2737
 
6.9%
2270
 
5.7%
2175
 
5.4%
1004
 
2.5%
Other values (355) 14163
35.4%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
CJK
ValueCountFrequency (%)
1
100.0%
Distinct2508
Distinct (%)91.7%
Missing1
Missing (%)< 0.1%
Memory size21.5 KiB
2024-05-11T15:32:34.241757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length54
Median length47
Mean length33.157952
Min length19

Characters and Unicode

Total characters90687
Distinct characters583
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2401 ?
Unique (%)87.8%

Sample

1st row서울특별시 강북구 솔샘로 174, 135동 (미아동, SK북한산시티아파트)
2nd row서울특별시 강북구 도봉로 52, 연이빌딩 (미아동)
3rd row서울특별시 성동구 성수일로4길 25, 서울숲코오롱디지털타워 (성수동2가)
4th row서울특별시 구로구 우마2길 35, 가리봉주민센터 (가리봉동)
5th row서울특별시 노원구 광운로 20 (월계동, 광운대학교)
ValueCountFrequency (%)
서울특별시 2734
 
16.5%
지하 218
 
1.3%
노원구 199
 
1.2%
강서구 169
 
1.0%
중랑구 163
 
1.0%
서초구 145
 
0.9%
서대문구 139
 
0.8%
송파구 136
 
0.8%
성북구 124
 
0.7%
성동구 123
 
0.7%
Other values (4504) 12447
75.0%
2024-05-11T15:32:35.931772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13886
 
15.3%
3683
 
4.1%
3513
 
3.9%
3016
 
3.3%
3003
 
3.3%
2912
 
3.2%
2839
 
3.1%
) 2762
 
3.0%
( 2762
 
3.0%
2742
 
3.0%
Other values (573) 49569
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 58465
64.5%
Space Separator 13886
 
15.3%
Decimal Number 9812
 
10.8%
Close Punctuation 2763
 
3.0%
Open Punctuation 2763
 
3.0%
Other Punctuation 2459
 
2.7%
Dash Punctuation 210
 
0.2%
Uppercase Letter 210
 
0.2%
Math Symbol 62
 
0.1%
Lowercase Letter 54
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3683
 
6.3%
3513
 
6.0%
3016
 
5.2%
3003
 
5.1%
2912
 
5.0%
2839
 
4.9%
2742
 
4.7%
2741
 
4.7%
1243
 
2.1%
1212
 
2.1%
Other values (508) 31561
54.0%
Uppercase Letter
ValueCountFrequency (%)
S 30
14.3%
C 27
12.9%
K 26
12.4%
D 24
11.4%
M 23
11.0%
T 12
 
5.7%
R 7
 
3.3%
V 6
 
2.9%
E 6
 
2.9%
A 6
 
2.9%
Other values (13) 43
20.5%
Lowercase Letter
ValueCountFrequency (%)
e 19
35.2%
o 6
 
11.1%
n 4
 
7.4%
r 3
 
5.6%
l 3
 
5.6%
a 3
 
5.6%
t 3
 
5.6%
m 2
 
3.7%
p 2
 
3.7%
s 2
 
3.7%
Other values (7) 7
 
13.0%
Decimal Number
ValueCountFrequency (%)
1 2042
20.8%
2 1458
14.9%
3 1198
12.2%
5 903
9.2%
4 898
9.2%
6 746
 
7.6%
0 721
 
7.3%
7 671
 
6.8%
8 622
 
6.3%
9 553
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 2445
99.4%
. 6
 
0.2%
/ 4
 
0.2%
& 3
 
0.1%
! 1
 
< 0.1%
Letter Number
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 2762
> 99.9%
] 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 2762
> 99.9%
[ 1
 
< 0.1%
Space Separator
ValueCountFrequency (%)
13886
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 210
100.0%
Math Symbol
ValueCountFrequency (%)
~ 62
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 58464
64.5%
Common 31955
35.2%
Latin 267
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3683
 
6.3%
3513
 
6.0%
3016
 
5.2%
3003
 
5.1%
2912
 
5.0%
2839
 
4.9%
2742
 
4.7%
2741
 
4.7%
1243
 
2.1%
1212
 
2.1%
Other values (507) 31560
54.0%
Latin
ValueCountFrequency (%)
S 30
 
11.2%
C 27
 
10.1%
K 26
 
9.7%
D 24
 
9.0%
M 23
 
8.6%
e 19
 
7.1%
T 12
 
4.5%
R 7
 
2.6%
V 6
 
2.2%
o 6
 
2.2%
Other values (33) 87
32.6%
Common
ValueCountFrequency (%)
13886
43.5%
) 2762
 
8.6%
( 2762
 
8.6%
, 2445
 
7.7%
1 2042
 
6.4%
2 1458
 
4.6%
3 1198
 
3.7%
5 903
 
2.8%
4 898
 
2.8%
6 746
 
2.3%
Other values (12) 2855
 
8.9%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 58464
64.5%
ASCII 32219
35.5%
Number Forms 3
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13886
43.1%
) 2762
 
8.6%
( 2762
 
8.6%
, 2445
 
7.6%
1 2042
 
6.3%
2 1458
 
4.5%
3 1198
 
3.7%
5 903
 
2.8%
4 898
 
2.8%
6 746
 
2.3%
Other values (52) 3119
 
9.7%
Hangul
ValueCountFrequency (%)
3683
 
6.3%
3513
 
6.0%
3016
 
5.2%
3003
 
5.1%
2912
 
5.0%
2839
 
4.9%
2742
 
4.7%
2741
 
4.7%
1243
 
2.1%
1212
 
2.1%
Other values (507) 31560
54.0%
Number Forms
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
CJK
ValueCountFrequency (%)
1
100.0%

도로명우편번호
Text

MISSING 

Distinct1895
Distinct (%)70.7%
Missing56
Missing (%)2.0%
Memory size21.5 KiB
2024-05-11T15:32:36.348046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length4.7835821
Min length4

Characters and Unicode

Total characters12820
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1401 ?
Unique (%)52.3%

Sample

1st row01192
2nd row01215
3rd row04781
4th row08383
5th row01897
ValueCountFrequency (%)
08725 15
 
0.6%
2831 11
 
0.4%
01192 10
 
0.4%
3709 10
 
0.4%
01191 9
 
0.3%
08862 9
 
0.3%
08845 8
 
0.3%
01882 8
 
0.3%
3730 8
 
0.3%
07519 8
 
0.3%
Other values (1885) 2584
96.4%
2024-05-11T15:32:36.918921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2833
22.1%
7 1395
10.9%
1 1330
10.4%
3 1291
10.1%
5 1107
 
8.6%
6 1075
 
8.4%
2 1073
 
8.4%
4 1002
 
7.8%
8 995
 
7.8%
9 644
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12745
99.4%
Dash Punctuation 75
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2833
22.2%
7 1395
10.9%
1 1330
10.4%
3 1291
10.1%
5 1107
 
8.7%
6 1075
 
8.4%
2 1073
 
8.4%
4 1002
 
7.9%
8 995
 
7.8%
9 644
 
5.1%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2833
22.1%
7 1395
10.9%
1 1330
10.4%
3 1291
10.1%
5 1107
 
8.6%
6 1075
 
8.4%
2 1073
 
8.4%
4 1002
 
7.8%
8 995
 
7.8%
9 644
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2833
22.1%
7 1395
10.9%
1 1330
10.4%
3 1291
10.1%
5 1107
 
8.6%
6 1075
 
8.4%
2 1073
 
8.4%
4 1002
 
7.8%
8 995
 
7.8%
9 644
 
5.0%
Distinct2720
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
2024-05-11T15:32:37.237482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length17.943713
Min length3

Characters and Unicode

Total characters49094
Distinct characters556
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2707 ?
Unique (%)98.9%

Sample

1st rowSK북한산시티아파트(P207, 135동)(지하3층)
2nd row와이스퀘어(지하3~4층)
3rd row서울숲 코오롱디지털타워1차(지하1~3층)
4th row가리봉동주민센터 지하1층 주차장, 지하2층 주차장
5th row광운대학교 지하2~3층 주차장
ValueCountFrequency (%)
지하주차장 677
 
9.0%
지하1층 570
 
7.6%
주차장 418
 
5.6%
1층 411
 
5.5%
지하 210
 
2.8%
지하1~2층 177
 
2.4%
1~2층 165
 
2.2%
지하1~3층 92
 
1.2%
1~3층 69
 
0.9%
지하2층 62
 
0.8%
Other values (3107) 4632
61.9%
2024-05-11T15:32:37.823588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4771
 
9.7%
3406
 
6.9%
3113
 
6.3%
1 2895
 
5.9%
2756
 
5.6%
1799
 
3.7%
1726
 
3.5%
1699
 
3.5%
1441
 
2.9%
1365
 
2.8%
Other values (546) 24123
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 34667
70.6%
Decimal Number 5963
 
12.1%
Space Separator 4771
 
9.7%
Math Symbol 1152
 
2.3%
Open Punctuation 941
 
1.9%
Close Punctuation 934
 
1.9%
Uppercase Letter 314
 
0.6%
Other Punctuation 239
 
0.5%
Dash Punctuation 82
 
0.2%
Lowercase Letter 31
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3406
 
9.8%
3113
 
9.0%
2756
 
7.9%
1799
 
5.2%
1726
 
5.0%
1699
 
4.9%
1441
 
4.2%
1365
 
3.9%
1353
 
3.9%
986
 
2.8%
Other values (498) 15023
43.3%
Uppercase Letter
ValueCountFrequency (%)
C 36
11.5%
A 34
10.8%
S 31
9.9%
P 30
9.6%
M 29
9.2%
B 29
9.2%
D 28
8.9%
K 28
8.9%
T 18
5.7%
G 7
 
2.2%
Other values (13) 44
14.0%
Decimal Number
ValueCountFrequency (%)
1 2895
48.5%
2 1314
22.0%
3 641
 
10.7%
0 342
 
5.7%
4 276
 
4.6%
5 174
 
2.9%
6 124
 
2.1%
7 78
 
1.3%
9 69
 
1.2%
8 50
 
0.8%
Other Punctuation
ValueCountFrequency (%)
, 219
91.6%
@ 6
 
2.5%
/ 5
 
2.1%
. 4
 
1.7%
? 3
 
1.3%
: 2
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 15
48.4%
k 7
22.6%
s 7
22.6%
p 2
 
6.5%
Space Separator
ValueCountFrequency (%)
4771
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1152
100.0%
Open Punctuation
ValueCountFrequency (%)
( 941
100.0%
Close Punctuation
ValueCountFrequency (%)
) 934
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 34667
70.6%
Common 14082
28.7%
Latin 345
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3406
 
9.8%
3113
 
9.0%
2756
 
7.9%
1799
 
5.2%
1726
 
5.0%
1699
 
4.9%
1441
 
4.2%
1365
 
3.9%
1353
 
3.9%
986
 
2.8%
Other values (498) 15023
43.3%
Latin
ValueCountFrequency (%)
C 36
10.4%
A 34
9.9%
S 31
9.0%
P 30
8.7%
M 29
8.4%
B 29
8.4%
D 28
8.1%
K 28
8.1%
T 18
 
5.2%
e 15
 
4.3%
Other values (17) 67
19.4%
Common
ValueCountFrequency (%)
4771
33.9%
1 2895
20.6%
2 1314
 
9.3%
~ 1152
 
8.2%
( 941
 
6.7%
) 934
 
6.6%
3 641
 
4.6%
0 342
 
2.4%
4 276
 
2.0%
, 219
 
1.6%
Other values (11) 597
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 34667
70.6%
ASCII 14427
29.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4771
33.1%
1 2895
20.1%
2 1314
 
9.1%
~ 1152
 
8.0%
( 941
 
6.5%
) 934
 
6.5%
3 641
 
4.4%
0 342
 
2.4%
4 276
 
1.9%
, 219
 
1.5%
Other values (38) 942
 
6.5%
Hangul
ValueCountFrequency (%)
3406
 
9.8%
3113
 
9.0%
2756
 
7.9%
1799
 
5.2%
1726
 
5.0%
1699
 
4.9%
1441
 
4.2%
1365
 
3.9%
1353
 
3.9%
986
 
2.8%
Other values (498) 15023
43.3%
Distinct2733
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
Minimum2008-12-18 17:12:22
Maximum2024-03-19 19:42:28
2024-05-11T15:32:38.033112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:38.254365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터갱신구분
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
U
2630 
I
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowU
2nd rowU
3rd rowI
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 2630
96.1%
I 106
 
3.9%

Length

2024-05-11T15:32:38.445043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:38.570701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
u 2630
96.1%
i 106
 
3.9%
Distinct215
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
Minimum2018-08-31 23:59:59
Maximum2024-04-18 13:09:08.289765
2024-05-11T15:32:38.702953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T15:32:38.914505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

업태구분명
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2736
Missing (%)100.0%
Memory size24.2 KiB

좌표정보(X)
Real number (ℝ)

MISSING  SKEWED 

Distinct2386
Distinct (%)89.1%
Missing57
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean199281.63
Minimum182524.82
Maximum940083.76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2024-05-11T15:32:39.121036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182524.82
5-th percentile185737.28
Q1192880.7
median200443.57
Q3204937.2
95-th percentile210789.07
Maximum940083.76
Range757558.94
Interquartile range (IQR)12056.503

Descriptive statistics

Standard deviation16212.539
Coefficient of variation (CV)0.081354912
Kurtosis1628.5429
Mean199281.63
Median Absolute Deviation (MAD)5669.2713
Skewness35.62348
Sum5.3387548 × 108
Variance2.6284643 × 108
MonotonicityNot monotonic
2024-05-11T15:32:39.332784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195563.161650411 15
 
0.5%
201045.611312321 14
 
0.5%
200841.726990037 13
 
0.5%
200886.333781015 11
 
0.4%
192855.685643844 8
 
0.3%
194584.959249312 7
 
0.3%
195259.429272709 7
 
0.3%
195477.913016234 7
 
0.3%
185034.346957949 7
 
0.3%
193957.944323928 7
 
0.3%
Other values (2376) 2583
94.4%
(Missing) 57
 
2.1%
ValueCountFrequency (%)
182524.823835629 2
0.1%
182770.934381818 1
 
< 0.1%
182929.561795629 1
 
< 0.1%
182977.186875111 1
 
< 0.1%
183008.380596155 1
 
< 0.1%
183111.12908033 1
 
< 0.1%
183160.781059782 1
 
< 0.1%
183169.082562086 4
0.1%
183187.881462944 1
 
< 0.1%
183195.395147438 1
 
< 0.1%
ValueCountFrequency (%)
940083.759083 1
< 0.1%
241340.174804266 1
< 0.1%
215966.729045494 2
0.1%
215927.688523964 1
< 0.1%
215898.113091143 1
< 0.1%
215496.149626989 1
< 0.1%
215423.76462869 1
< 0.1%
215422.746119624 1
< 0.1%
215144.766442481 1
< 0.1%
213999.956783491 1
< 0.1%

좌표정보(Y)
Real number (ℝ)

MISSING  SKEWED 

Distinct2386
Distinct (%)89.1%
Missing57
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean451038.63
Minimum437680.11
Maximum1952577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2024-05-11T15:32:39.571204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437680.11
5-th percentile441437.77
Q1445231.72
median450416.39
Q3455013.64
95-th percentile461420.16
Maximum1952577
Range1514896.9
Interquartile range (IQR)9781.9247

Descriptive statistics

Standard deviation29667.374
Coefficient of variation (CV)0.065775684
Kurtosis2452.7941
Mean451038.63
Median Absolute Deviation (MAD)4888.0854
Skewness48.448746
Sum1.2083325 × 109
Variance8.8015311 × 108
MonotonicityNot monotonic
2024-05-11T15:32:39.833910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
443064.437566493 15
 
0.5%
457292.961457264 14
 
0.5%
454721.505180141 13
 
0.5%
457645.184347648 11
 
0.4%
439663.585570958 8
 
0.3%
451381.585492051 7
 
0.3%
453430.811542203 7
 
0.3%
453929.325030582 7
 
0.3%
449164.972451344 7
 
0.3%
441437.774867372 7
 
0.3%
Other values (2376) 2583
94.4%
(Missing) 57
 
2.1%
ValueCountFrequency (%)
437680.1128998 1
< 0.1%
437795.951394135 1
< 0.1%
437914.06299827 1
< 0.1%
438303.705016298 1
< 0.1%
438410.522111193 2
0.1%
438423.909374917 1
< 0.1%
438436.109390506 1
< 0.1%
438458.097193512 1
< 0.1%
438460.667319682 1
< 0.1%
438483.387454937 1
< 0.1%
ValueCountFrequency (%)
1952577.039191 1
< 0.1%
477863.793793022 1
< 0.1%
465158.799624502 1
< 0.1%
465025.246646886 1
< 0.1%
464905.033086911 1
< 0.1%
464849.968985063 1
< 0.1%
464814.717432497 1
< 0.1%
464804.016663922 1
< 0.1%
464709.524245203 1
< 0.1%
464663.321565455 1
< 0.1%

비상시설위치
Text

MISSING 

Distinct751
Distinct (%)88.4%
Missing1886
Missing (%)68.9%
Memory size21.5 KiB
2024-05-11T15:32:40.454636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length21.814118
Min length17

Characters and Unicode

Total characters18542
Distinct characters295
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique703 ?
Unique (%)82.7%

Sample

1st row서울특별시 마포구 도화동 174번지 5호
2nd row서울특별시 마포구 도화동 553번지 마스터즈타워빌딩
3rd row서울특별시 강동구 둔촌동 522번지 9호 한국전력 강동송파지사
4th row서울특별시 도봉구 창동 347-5 창1동 주민센터
5th row서울특별시 송파구 잠실동 230-4
ValueCountFrequency (%)
서울특별시 849
 
21.8%
중랑구 107
 
2.7%
양천구 92
 
2.4%
송파구 80
 
2.1%
1호 74
 
1.9%
서대문구 70
 
1.8%
은평구 64
 
1.6%
서초구 60
 
1.5%
성북구 50
 
1.3%
영등포구 45
 
1.2%
Other values (1007) 2411
61.8%
2024-05-11T15:32:41.115886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3052
 
16.5%
1000
 
5.4%
943
 
5.1%
890
 
4.8%
872
 
4.7%
859
 
4.6%
850
 
4.6%
850
 
4.6%
749
 
4.0%
711
 
3.8%
Other values (285) 7766
41.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12333
66.5%
Space Separator 3052
 
16.5%
Decimal Number 3042
 
16.4%
Dash Punctuation 61
 
0.3%
Uppercase Letter 42
 
0.2%
Lowercase Letter 8
 
< 0.1%
Math Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1000
 
8.1%
943
 
7.6%
890
 
7.2%
872
 
7.1%
859
 
7.0%
850
 
6.9%
850
 
6.9%
749
 
6.1%
711
 
5.8%
290
 
2.4%
Other values (253) 4319
35.0%
Uppercase Letter
ValueCountFrequency (%)
C 10
23.8%
D 9
21.4%
M 9
21.4%
T 2
 
4.8%
I 2
 
4.8%
E 1
 
2.4%
W 1
 
2.4%
S 1
 
2.4%
K 1
 
2.4%
V 1
 
2.4%
Other values (5) 5
11.9%
Decimal Number
ValueCountFrequency (%)
1 646
21.2%
2 329
10.8%
3 310
10.2%
4 288
9.5%
6 271
8.9%
5 265
8.7%
0 257
 
8.4%
9 234
 
7.7%
7 232
 
7.6%
8 210
 
6.9%
Space Separator
ValueCountFrequency (%)
3052
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 61
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12332
66.5%
Common 6159
33.2%
Latin 50
 
0.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1000
 
8.1%
943
 
7.6%
890
 
7.2%
872
 
7.1%
859
 
7.0%
850
 
6.9%
850
 
6.9%
749
 
6.1%
711
 
5.8%
290
 
2.4%
Other values (252) 4318
35.0%
Common
ValueCountFrequency (%)
3052
49.6%
1 646
 
10.5%
2 329
 
5.3%
3 310
 
5.0%
4 288
 
4.7%
6 271
 
4.4%
5 265
 
4.3%
0 257
 
4.2%
9 234
 
3.8%
7 232
 
3.8%
Other values (6) 275
 
4.5%
Latin
ValueCountFrequency (%)
C 10
20.0%
D 9
18.0%
M 9
18.0%
e 8
16.0%
T 2
 
4.0%
I 2
 
4.0%
E 1
 
2.0%
W 1
 
2.0%
S 1
 
2.0%
K 1
 
2.0%
Other values (6) 6
12.0%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12332
66.5%
ASCII 6209
33.5%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3052
49.2%
1 646
 
10.4%
2 329
 
5.3%
3 310
 
5.0%
4 288
 
4.6%
6 271
 
4.4%
5 265
 
4.3%
0 257
 
4.1%
9 234
 
3.8%
7 232
 
3.7%
Other values (22) 325
 
5.2%
Hangul
ValueCountFrequency (%)
1000
 
8.1%
943
 
7.6%
890
 
7.2%
872
 
7.1%
859
 
7.0%
850
 
6.9%
850
 
6.9%
749
 
6.1%
711
 
5.8%
290
 
2.4%
Other values (252) 4318
35.0%
CJK
ValueCountFrequency (%)
1
100.0%

시설구분명
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size21.5 KiB
<NA>
1886 
공공용시설
755 
공공시설
 
95

Length

Max length5
Median length4
Mean length4.2759503
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 1886
68.9%
공공용시설 755
27.6%
공공시설 95
 
3.5%

Length

2024-05-11T15:32:41.313576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T15:32:41.461043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1886
68.9%
공공용시설 755
27.6%
공공시설 95
 
3.5%

시설명_건물명
Text

MISSING 

Distinct845
Distinct (%)99.4%
Missing1886
Missing (%)68.9%
Memory size21.5 KiB
2024-05-11T15:32:41.751632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23
Mean length17.910588
Min length3

Characters and Unicode

Total characters15224
Distinct characters432
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique842 ?
Unique (%)99.1%

Sample

1st row삼창프라자
2nd row마스터즈 빌딩
3rd row한전강동지점
4th row창1동 주민센터 지하주차장 1층
5th row잠실본동 복합청사 지하2층
ValueCountFrequency (%)
지하1층 179
 
7.7%
지하주차장 167
 
7.2%
주차장 142
 
6.1%
1층 118
 
5.1%
지하1~2층 63
 
2.7%
지하 61
 
2.6%
1~2층 48
 
2.1%
1~3층 29
 
1.3%
동의 27
 
1.2%
지하1,2층 26
 
1.1%
Other values (1072) 1458
62.9%
2024-05-11T15:32:42.374237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1481
 
9.7%
994
 
6.5%
1 932
 
6.1%
899
 
5.9%
778
 
5.1%
562
 
3.7%
528
 
3.5%
512
 
3.4%
495
 
3.3%
493
 
3.2%
Other values (422) 7550
49.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10749
70.6%
Decimal Number 1885
 
12.4%
Space Separator 1481
 
9.7%
Math Symbol 311
 
2.0%
Open Punctuation 287
 
1.9%
Close Punctuation 283
 
1.9%
Other Punctuation 104
 
0.7%
Uppercase Letter 87
 
0.6%
Dash Punctuation 19
 
0.1%
Lowercase Letter 18
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
994
 
9.2%
899
 
8.4%
778
 
7.2%
562
 
5.2%
528
 
4.9%
512
 
4.8%
495
 
4.6%
493
 
4.6%
490
 
4.6%
323
 
3.0%
Other values (380) 4675
43.5%
Uppercase Letter
ValueCountFrequency (%)
C 15
17.2%
D 12
13.8%
M 11
12.6%
B 11
12.6%
P 6
 
6.9%
T 5
 
5.7%
K 4
 
4.6%
I 4
 
4.6%
S 3
 
3.4%
A 3
 
3.4%
Other values (9) 13
14.9%
Decimal Number
ValueCountFrequency (%)
1 932
49.4%
2 379
20.1%
3 163
 
8.6%
0 149
 
7.9%
4 78
 
4.1%
5 56
 
3.0%
6 42
 
2.2%
9 32
 
1.7%
7 30
 
1.6%
8 24
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 97
93.3%
@ 4
 
3.8%
/ 1
 
1.0%
? 1
 
1.0%
. 1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
e 10
55.6%
k 4
 
22.2%
s 4
 
22.2%
Space Separator
ValueCountFrequency (%)
1481
100.0%
Math Symbol
ValueCountFrequency (%)
~ 311
100.0%
Open Punctuation
ValueCountFrequency (%)
( 287
100.0%
Close Punctuation
ValueCountFrequency (%)
) 283
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10749
70.6%
Common 4370
28.7%
Latin 105
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
994
 
9.2%
899
 
8.4%
778
 
7.2%
562
 
5.2%
528
 
4.9%
512
 
4.8%
495
 
4.6%
493
 
4.6%
490
 
4.6%
323
 
3.0%
Other values (380) 4675
43.5%
Latin
ValueCountFrequency (%)
C 15
14.3%
D 12
11.4%
M 11
10.5%
B 11
10.5%
e 10
9.5%
P 6
 
5.7%
T 5
 
4.8%
K 4
 
3.8%
I 4
 
3.8%
k 4
 
3.8%
Other values (12) 23
21.9%
Common
ValueCountFrequency (%)
1481
33.9%
1 932
21.3%
2 379
 
8.7%
~ 311
 
7.1%
( 287
 
6.6%
) 283
 
6.5%
3 163
 
3.7%
0 149
 
3.4%
, 97
 
2.2%
4 78
 
1.8%
Other values (10) 210
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10749
70.6%
ASCII 4475
29.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1481
33.1%
1 932
20.8%
2 379
 
8.5%
~ 311
 
6.9%
( 287
 
6.4%
) 283
 
6.3%
3 163
 
3.6%
0 149
 
3.3%
, 97
 
2.2%
4 78
 
1.7%
Other values (32) 315
 
7.0%
Hangul
ValueCountFrequency (%)
994
 
9.2%
899
 
8.4%
778
 
7.2%
562
 
5.2%
528
 
4.9%
512
 
4.8%
495
 
4.6%
493
 
4.6%
490
 
4.6%
323
 
3.0%
Other values (380) 4675
43.5%

해제일자
Real number (ℝ)

MISSING 

Distinct40
Distinct (%)42.1%
Missing2641
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean20165785
Minimum20081218
Maximum20230618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2024-05-11T15:32:42.557133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20081218
5-th percentile20140527
Q120160201
median20161226
Q320171064
95-th percentile20209245
Maximum20230618
Range149400
Interquartile range (IQR)10862.5

Descriptive statistics

Standard deviation21203.809
Coefficient of variation (CV)0.0010514745
Kurtosis5.1530014
Mean20165785
Median Absolute Deviation (MAD)9681
Skewness0.72534857
Sum1.9157496 × 109
Variance4.496015 × 108
MonotonicityNot monotonic
2024-05-11T15:32:42.766727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
20171114 10
 
0.4%
20161226 10
 
0.4%
20160201 6
 
0.2%
20170119 5
 
0.2%
20170131 5
 
0.2%
20140627 4
 
0.1%
20161230 4
 
0.1%
20160902 3
 
0.1%
20150623 3
 
0.1%
20140526 3
 
0.1%
Other values (30) 42
 
1.5%
(Missing) 2641
96.5%
ValueCountFrequency (%)
20081218 1
 
< 0.1%
20131127 1
 
< 0.1%
20140526 3
0.1%
20140527 1
 
< 0.1%
20140627 4
0.1%
20140711 1
 
< 0.1%
20150622 1
 
< 0.1%
20150623 3
0.1%
20150626 1
 
< 0.1%
20150903 3
0.1%
ValueCountFrequency (%)
20230618 1
< 0.1%
20230616 1
< 0.1%
20230613 1
< 0.1%
20230327 2
0.1%
20200210 1
< 0.1%
20190802 1
< 0.1%
20190308 2
0.1%
20181126 1
< 0.1%
20180701 2
0.1%
20180627 1
< 0.1%

Sample

개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)비상시설위치시설구분명시설명_건물명해제일자
030800003080000-S2023000062023-07-31<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>2146.0<NA>서울특별시 강북구 미아동 1353 SK북한산시티아파트서울특별시 강북구 솔샘로 174, 135동 (미아동, SK북한산시티아파트)01192SK북한산시티아파트(P207, 135동)(지하3층)2024-01-06 20:08:45U2023-12-01 00:08:00.0<NA>201045.611312457292.961457<NA><NA><NA><NA>
130800003080000-S2024000012024-01-22<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>3299.13<NA>서울특별시 강북구 미아동 1364 연이빌딩서울특별시 강북구 도봉로 52, 연이빌딩 (미아동)01215와이스퀘어(지하3~4층)2024-01-30 12:33:59U2023-12-02 00:01:00.0<NA>202624.617263456818.852071<NA><NA><NA><NA>
230300003030000-S2023000272023-07-07<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>15205.47<NA>서울특별시 성동구 성수동2가 308-4 서울숲코오롱디지털타워서울특별시 성동구 성수일로4길 25, 서울숲코오롱디지털타워 (성수동2가)04781서울숲 코오롱디지털타워1차(지하1~3층)2023-07-18 13:44:25I2022-12-06 22:00:00.0<NA>204522.096092448925.421927<NA><NA><NA><NA>
331600003160000-S2022000042022-12-22<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>890.47<NA>서울특별시 구로구 가리봉동 118-11서울특별시 구로구 우마2길 35, 가리봉주민센터 (가리봉동)08383가리봉동주민센터 지하1층 주차장, 지하2층 주차장2024-02-02 09:45:24U2023-12-02 00:04:00.0<NA>190173.233224442268.896<NA><NA><NA><NA>
431000003100000-S2017000032017-05-23<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>12202.0<NA>서울특별시 노원구 월계동 447번지 1호서울특별시 노원구 광운로 20 (월계동, 광운대학교)01897광운대학교 지하2~3층 주차장2024-02-02 14:27:18U2023-12-02 00:04:00.0<NA>205198.498721457486.206509<NA><NA><NA><NA>
531000003100000-S2007000092007-06-01<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>637.0<NA>서울특별시 노원구 상계동 771 서울시립뇌성마비복지관서울특별시 노원구 덕릉로70가길 96, 서울시립뇌성마비복지관 (상계동)01772시립뇌성마비복지관 지하1층 식당2024-02-08 18:38:33U2023-12-01 23:01:00.0<NA>204940.089436459998.31488<NA><NA><NA><NA>
632400003240000-S2013000022013-06-26<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>17913.98<NA>서울특별시 강동구 상일동 149 강동경희대학교병원 본관 지하1층서울특별시 강동구 동남로 892, 강동경희대학교병원 본관동 지하 1층 (상일동)05278강동경희대학교 병원 본관동 지하1층2023-07-18 14:29:15U2022-12-06 22:00:00.0<NA>213839.356196450118.261708<NA><NA><NA><NA>
732000003200000-S2006000042006-01-10<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>11159.94<NA>서울특별시 관악구 봉천동 1719번지서울특별시 관악구 은천로33길 5 (봉천동)08729관악동부센트레빌 지하주차장 (지하1~3층)2024-02-08 14:08:41U2023-12-01 23:01:00.0<NA>195595.842766442779.951685<NA><NA><NA><NA>
832000003200000-S2005000892005-07-05<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>5805.0<NA>서울특별시 관악구 봉천동 1703번지서울특별시 관악구 관악로 285 (봉천동)08726성현동아아파트109동 지하주차장 (지하1층)2024-02-08 14:05:52U2023-12-01 23:01:00.0<NA>195999.574887443044.700996<NA><NA><NA><NA>
932000003200000-S2005000882005-07-05<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>13988.75<NA>서울특별시 관악구 봉천동 1703번지서울특별시 관악구 관악로 285 (봉천동)08726성현동아아파트106-8동 지하주차장 (지하1층)2024-02-08 14:04:42U2023-12-01 23:01:00.0<NA>195999.574887443044.700996<NA><NA><NA><NA>
개방자치단체코드관리번호인허가일자인허가취소일자영업상태코드영업상태명상세영업상태코드상세영업상태명폐업일자휴업시작일자휴업종료일자재개업일자전화번호소재지면적소재지우편번호지번주소도로명주소도로명우편번호사업장명최종수정일자데이터갱신구분데이터갱신일자업태구분명좌표정보(X)좌표정보(Y)비상시설위치시설구분명시설명_건물명해제일자
272631000003100000-S2023000012023-05-04<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>982.14<NA>서울특별시 노원구 공릉동 737 삼익아파트서울특별시 노원구 공릉로46길 32 (공릉동, 삼익아파트)01816공릉2동 삼익1차아파트 107동 지하1층 주차장2024-02-06 13:07:34U2023-12-02 00:08:00.0<NA>207164.201417458290.138258<NA><NA><NA><NA>
272731000003100000-S2023000022023-05-04<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>857.8<NA>서울특별시 노원구 공릉동 737 삼익아파트서울특별시 노원구 공릉로46길 32 (공릉동, 삼익아파트)01816공릉2동 삼익1차아파트 103동 지하1층 주차장2024-02-06 13:08:08U2023-12-02 00:08:00.0<NA>207164.201417458290.138258<NA><NA><NA><NA>
272831000003100000-S2012000132012-04-25<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>600.0<NA>서울특별시 노원구 공릉동 111번지서울특별시 노원구 화랑로51길 17 (공릉동, 화랑타운아파트)01800공릉2동 화랑타운아파트 지하주차장 1층2024-02-06 13:06:55U2023-12-02 00:08:00.0<NA>207746.151245457922.120884<NA><NA><NA><NA>
272931000003100000-S2009000102009-11-04<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>13500.0<NA>서울특별시 노원구 공릉동 741번지서울특별시 노원구 공릉로27길 110 (공릉동, 현대성우아파트)01834공릉2동 현대성우아파트 지하주차장 1~3층2024-02-06 13:06:20U2023-12-02 00:08:00.0<NA>206767.12663458240.757697<NA><NA><NA><NA>
273031000003100000-S2006000012006-07-14<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>330.0<NA>서울특별시 노원구 공릉동 87번지 7호서울특별시 노원구 노원로1길 68 (공릉동, 공릉2동청사)01823공릉2동 주민센터 지하 1층2024-02-06 13:00:44U2023-12-02 00:08:00.0<NA>207290.202272457665.819152<NA><NA><NA><NA>
273131000003100000-S2005000042005-02-17<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>15156.28<NA>서울특별시 노원구 상계동 713번지서울특별시 노원구 동일로 1414 (상계동, 롯데백화점)01695롯데백화점 노원점 지하2,3층 주차장2024-02-06 11:21:41U2023-12-02 00:08:00.0<NA>205320.284767461419.881795<NA><NA><NA><NA>
273231000003100000-S2001000022001-01-01<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>80.4<NA>서울특별시 노원구 공릉동 221번지 3호서울특별시 노원구 공릉로 166-1 (공릉동)01818공릉2동 공릉보건지소 지하 1층2024-02-06 15:59:05U2023-12-02 00:08:00.0<NA>206992.984879458054.70906<NA><NA><NA><NA>
273331000003100000-S2001000062001-01-01<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>35000.0<NA>서울특별시 노원구 공릉동 285번지 2호서울특별시 노원구 화랑로 지하 510 (공릉동)01804공릉2동 화랑대역 지하1,2층2024-02-06 13:02:29U2023-12-02 00:08:00.0<NA>207283.444093457509.134932<NA><NA><NA><NA>
273431000003100000-S2001000092001-01-01<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>1200.0<NA>서울특별시 노원구 공릉동 420번지 2호서울특별시 노원구 공릉로46길 3 (공릉동)01814공릉2동 평화타운 지하 1~3층2024-02-06 13:02:58U2023-12-02 00:08:00.0<NA>206918.939595458317.423099<NA><NA><NA><NA>
273531000003100000-S2001000132001-01-01<NA>1영업/정상18사용중<NA><NA><NA><NA><NA>14000.0<NA>서울특별시 노원구 공릉동 81번지서울특별시 노원구 공릉로34길 62 (공릉동, 태강아파트)01820공릉2동 태강아파트 지하주차장 1,2층2024-02-06 13:05:27U2023-12-02 00:08:00.0<NA>207274.300313457909.125969<NA><NA><NA><NA>